Researchers have developed a maximum power -point tracking -algorithm based on the social hierarchy and hunting strategy of gray wolves. When tested under realistic shadow conditions, the gray wolf -optimizer reached an average MPPT efficiency of 98.15%, which performed considerably better than conventional MPPT methods.
A team of scientists led by researchers from Abdul Latif University in Pakistan has developed a maximum power point tracking (MPPT) algorithm that uses a Gray Wolf Optimizer (GWO) under realistic shadow conditions. The GWO is a bio-inspired algorithm that uses the social hierarchy and hunting strategy of gray wolves.
Corresponding author Syed Hadi Hussain Shah told PV -Magazine The work shows that a simple GWO, immediately implemented on the Duty cycle with a very low calculation question, can reliably follow the global maximum power point under partial shadow. “The novelty lies in proving that a lightweight, non-hybridist can achieve metahuristically fast and repeatable results that are practical for embedded controllers,” he added.
The GWO-based MPPT uses the prey of the Wolf package as the maximum power point (MPP). The package starts by searching for prey or, in the context of a PV system, to try different work cycle values and ultimately close the best position, the one who delivers the highest force. Ultimately, the entire package of candidate -Duty Cycle values surrounded the ‘prey’ and converters it on the Global Maximum Power Point (GMPP).
The GWO MPPT was tested in Matlab/Simulink on a simulated PV -Array consisting of four modules connected in series. Each module consisted of 60 cells and had a nominal maximum capacity of 250 W. A DC-DC-Boost-Omgetter for optimized power extraction and voltage regulation was also simulated, together with a tax resistance that represents the system consumption of the system.
It was tested under three scenarios: uniform radiation conditions with 1,000 W/m2; Drinking shadow, which used irradiation of 1,000 W/m2, 1,000 W/m2, 800 W/m2 and 500 W/m2, or 500 W/m2, 800 W/m2, 1,000 W/m2 and 1,000 W/m2; Or any shadow condition, with dynamically varying irradiation over different PV modules. Temperatures were constantly kept on 25 C.
The results of the GWO MPPT were compared with those of other techniques, namely Perturb and Observing (P&O), Incremental conduction (Inc), particle sewing optimization (PSO), Whale Optimization Algorithm (WOA), Moth Flame optimization (Anfis) (Anfis).
“Each MPPT algorithm was tested over 10 independent runs per shadow condition to guarantee statistical robustness. Performance was evaluated in terms of MPPT efficiency, convergence time and stable states,” the research team explained. “Resultaten toonden aan dat GWO een gemiddelde MPPT-efficiëntie van 98,15%behaalde, aanzienlijk outperformerend Inc (74%) en P&O (54%). Het GWO-algoritme convergeerde naar de GMPP in slechts 0,06 s met minimale oscillaties (~ 2 W), terwijl conventionele methoden de lezers en hogere oscillaties en hogere oscillaties en hogere Oscillations and higher oscillations have shown superiority of GWO (p <0.0001) on both shadow scenarios, without significant decrease in efficiency.
Shah added that the team was surprised by how consistently GWO performed better than conventional MPPT methods. “It converged to the global maximum in around 60 ms with minimal wrinkle, while incremental conduction and P&O were trapped on local peaks and 20-40% of the available force lost,” he said. “The repeatability and statistical significance of the improvement were striking.”
The algorithm was presented in the research paper Comparative analysis of GWO MPPT with conventional techniques in shady PV -ArraysPublished in Results in Engineering. Scientists from Abdul Latif University in Pakistan, Sukkur IBA University and China’s Powerchina Huandong Engineering Corporation have contributed to the work.
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